Prediction of mortality risk in patients with peripheral artery disease using interpretable machine learning.

BACKGROUND: Patients with peripheral artery disease (PAD) face high postoperative mortality risks, necessitating precise risk stratification. While machine learning offers superior performance, its black-box nature limits clinical utility, and the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) remains controversial. METHODS: A total of 610 surgically managed PAD patients were enrolled (median follow-up: 4 years) and randomly split into training (70%) and test (30%) sets. Six machine learning algorithms were constructed and optimized. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The sHapley additive exPlanations (SHAP) were employed for model interpretation and visualizing nonlinear relationships. RESULTS: The random forest model achieved optimal performance (test set AUC = 0.814) with significant clinical net benefit. SHAP analysis identified age, prothrombin activity, and Rutherford classification as top predictors. Notably, while multivariate Cox regression failed to identify NLR as a linear predictor, SHAP dependence plots revealed a distinct nonlinear pattern: risk contribution increased sharply at low standardized NLR values before plateauing. CONCLUSION: We established an interpretable random forest model for predicting postoperative mortality in PAD. By integrating SHAP analysis, this study validates the nonlinear prognostic significance of NLR and demonstrates how explainable ML can complement traditional statistics for individualized risk assessment.